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Iterative Vessel Segmentation with Stopping Criterion for Fundus Imagery

J. Kanimozhi1 , P. Vasuki2 , S. Mohamed Mansoor Roomi3

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.3 , pp.6-12, Jun-2018

Online published on Jun 30, 2018


Copyright © J. Kanimozhi, P. Vasuki, S. Mohamed Mansoor Roomi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Citation :
IEEE Style Citation: J. Kanimozhi, P. Vasuki, S. Mohamed Mansoor Roomi, “Iterative Vessel Segmentation with Stopping Criterion for Fundus Imagery”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.6-12, 2018.

MLA Style Citation: J. Kanimozhi, P. Vasuki, S. Mohamed Mansoor Roomi "Iterative Vessel Segmentation with Stopping Criterion for Fundus Imagery." International Journal of Scientific Research in Network Security and Communication 6.3 (2018): 6-12.

APA Style Citation: J. Kanimozhi, P. Vasuki, S. Mohamed Mansoor Roomi, (2018). Iterative Vessel Segmentation with Stopping Criterion for Fundus Imagery. International Journal of Scientific Research in Network Security and Communication, 6(3), 6-12.

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Abstract :
Vessel segmentation in fundus images plays vital role in diagnosing and treating patients in Ophthalmology. This proposed vessel segmentation algorithm consists of three stages to improve the lower contrast fundus images includes enhancement followed by thresholding and segmentation. Adaptive histogram equalization method is used to enhance the input image. From the enhanced image the major vessel are extracted by thresholding using gray thresh method. The new vessel pixels are identified iteratively using region growing method in which a new stopping criterion is introduced to improve the accuracy. The proposed method outperforms than the existing method of iterative vessel segmentation which achieves 3% greater in accuracy.

Key-Words / Index Term :
Contrast enhancement; Histogram equalization; Segmentation; Stopping criterion; Accuracy; ROC

References :
[1] Abramoff, M.D (Jan 2010) “Retinal imaging and image analysis,” IEEE Trans.Med. Imag., vol. 3, pp. 169–208.
[2] Budai, A. (Mar 2010) “Multiscale blood vessel segmentation in retinal fundus images,” in Proc. Bildverarbeitung fr die Med., pp. 261–265.
[3] Budai A. (2013)“Robust vessel segmentation in fundus images,” Int. J. Biomed. Imag., vol. 2013.
[4] Diri, Al.B (Sep 2009) “An active contour model for segmenting and measuring retinal vessels,” IEEE Trans. Med. Imag., vol. 28, no. 9, pp. 1488–1497,
[5] Fraz, M. (2012) “Blood vessel segmentation methodologies in retinal images-a survey,” Comput. Methods Prog. Biomed., vol. 108,pp. 407–433.
[6] Fraz, M. (Jun 2012) “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Trans. Biomed. Eng., vol. 59, no. 9, pp. 2538–2548.
[7] Goatman, K. (Apr 2011) “Detection of new vessels on the optic disc using retinal photographs,” IEEE Trans. Med. Imag., vol. 30, no. 4, pp. 972–979.
[8] Gonzalez, R. C. and Woods, R. E. (1992) “Digital Image Processing”, 2nd ed. Boston, MA, USA: Addison-Wesley.
[9] Hoover, A.( Mar.2000) “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag., vol. 19, pp. 203–210.
[10] Jiang, X. and Mojon, D.(JAN 2003) “Adaptive local thresholding by verification based multithreshold probing with application to vessel detection in retinal images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 1, pp. 131-137.
[11] Karperien, A. (2008) “Automated detection of proliferative retinopathy in clinical practice,” Clin. Ophthalmol. (Auckland, NZ), vol. 2, no. 1, pp. 109–122.
[12] Kotliar, K. (2010) “Microstructural alterations of retinal arterial blood col-umn along the vessel axis in systemic hypertension,” Investigative Oph-thalmol. Vis. Sci., vol. 51, no. 4, pp. 2165–2172.
[13] Kochkorov, A.(2006) “Short-term retinal vessel diameter variability in relation to the history of cold extremities,” Investigative Ophthalmol. Vis. Sci., vol. 47, no. 9, pp. 4026–4033.
[14] Lupascu, C. (Sep 2010) “Fabc: Retinal vessel segmentation using adaboost,” IEEE Trans. Inform. Technol. Biomed., vol. 14, no. 5, pp. 1267–1274
[15] Lam, B. (Mar 2010) “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag., vol. 29, no. 7, pp.1369–1381.
[16] Lam, B. and Yan, H. (Feb 2008) “A novel vessel segmentation algorithm for patholog-ical retina images based on the divergence of vector fields,” IEEE Trans. Med. Imag., vol. 27, no. 2, pp. 237–246.
[17] Marin, D. (Jan 2011) “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag., vol. 30, no. 1, pp. 146–158.
[18] Mendonca, A. and Campilho, A. (Aug 2006) “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213.
[19] Miri, M. and Mahloojifar, A. (May 2011) “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng., vol. 58, no. 5, pp. 1183–1192.
[20] Niemeijer, M. (2004) “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. Med. Imag. SPIE, vol. 5370, pp. 648–656.
[21] Nagel, E. (2004) “Age, blood pressure, and vessel diameter as factors influencing the arterial retinal flicker response,” Investigative Ophthalmol. Vis. Sci., vol. 45, no. 5, pp. 1486–1492.
[22] Nguyen, U. T. V. (Mar 2013) “An effective retinal blood vessel segmentation method using multi-scale line detection,” Pattern Recogn., vol. 46, no. 3, pp. 703–715.
[23] Palomera-Perez, M. (Mar 2010) “Parallel multiscale feature extraction and re-gion growing: Application in retinal blood vessel detection,” IEEE Trans. Inform. Technol. Biomed., vol. 14, no. 2, pp. 500–506.
[24] Perfetti, R. (Feb 2007) “Cellular neural networks with virtual template expansion for retinal vessel segmentation,” IEEE Trans. Circuits Syst. II, Exp Briefs, vol. 54, no. 2, pp. 141–145.
[25] Pakter, H. M. (2011) “Computer-assisted methods to evaluate retinal vas-cular caliber: What are they measuring?” Investigative Ophthalmol. Vis. Sci., vol. 52, no. 2, pp. 810–815.
[26] Ricci, E. and Perfetti, R. (Oct 2007) “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag., vol. 26, no. 10, pp. 1357–1365.
[27] Roychowdhury, S. (Sep 2014) “Dream: Diabetic retinopathy analysis using machine learning,” IEEE J. Biomed. Health Informat., vol. 18, no. 5, pp. 1717–1728 .
[28] Roychowdhury, S. (May 2015) “Blood vessel segmentation of fundus images by major vessel extraction and sub-image classification,” IEEE J. Biomed. Health Informat., vol. 19, no. 3, pp. 1120-1127.
[29] Roychowdhury, S. (July 2015) “Iterative Vessel Segmentation of Fundus Images,” IEEE Trans. Biomed. Health Informat., vol. 62, no. 7, pp. 1738-1749.
[30] Soares, J. (Sep 2006) “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1214–1222.
[31] Staal, J. (Apr 2004) “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag., vol. 23, no. 4, pp. 501–509.
[32] Teng, T. (2002) “Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy,” Med. Biol. Eng. Comput., vol. 40, pp. 2–13,
[33] Vermeer, K. A. (2004) “A model based method for retinal blood vessel detection,” Comput. Biol. Med., vol. 34, no. 3, pp. 209–219.
[34] Wilson, C. M. (2008) “Computerized analysis of retinal vessel width and tortuosity in premature infants,” Investigative Ophthalmol. Vis. Sci., vol. 49, no. 8, pp. 3577–3585.
[35] Zana, F. and. Klein, J.C. (Jul 2001) “Segmentation of vessel-like patterns using math-ematical morphology and curvature evaluation,” IEEE Trans. Image Pro-cess., vol. 10, no. 7, pp. 1010–1019.
[36] Birendra Biswal,et.al.,” Robust retinal blood vessel segmentation using line detectors with multiple masks”, IET Image Processing Volume: 12, Issue: 3,pp. 389 – 399,2018.
[37] Xurong Gao; Yiheng Cai; Changyan Qiu; Yize Cui “Retinal blood vessel segmentation based on the Gaussian matched filter and U-net “2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI),pp.1-5,2017.

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